def load(self,
checkpoint_path,
num_speakers=2,
checkpoint_step=None,
model_name='tacotron'):
self.num_speakers = num_speakers
if os.path.isdir(checkpoint_path):
load_path = checkpoint_path
checkpoint_path = get_most_recent_checkpoint(
checkpoint_path, checkpoint_step)
else:
load_path = os.path.dirname(checkpoint_path)
print('Constructing model: %s' % model_name)
inputs = tf.placeholder(tf.int32, [None, None], 'inputs')
input_lengths = tf.placeholder(tf.int32, [None], 'input_lengths')
batch_size = tf.shape(inputs)[0]
speaker_id = tf.placeholder_with_default(
tf.zeros([batch_size], dtype=tf.int32), [None], 'speaker_id')
load_hparams(hparams, load_path)
with tf.variable_scope('model') as scope:
self.model = create_model(hparams)
self.model.initialize(inputs,
input_lengths,
self.num_speakers,
speaker_id,
rnn_decoder_test_mode=True)
self.wav_output = inv_spectrogram_tensorflow(
self.model.linear_outputs, hparams)
print('Loading checkpoint: %s' % checkpoint_path)
sess_config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=1,
inter_op_parallelism_threads=2)
sess_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=sess_config)
self.sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.sess, checkpoint_path)
def train(log_dir, config):
config.data_paths = config.data_paths # ['datasets/moon']
data_dirs = config.data_paths # ['datasets/moon\\data']
num_speakers = len(data_dirs)
config.num_test = config.num_test_per_speaker * num_speakers # 2*1
if num_speakers > 1 and hparams.model_type not in ["deepvoice", "simple"]:
raise Exception("[!] Unkown model_type for multi-speaker: {}".format(
config.model_type))
commit = get_git_commit() if config.git else 'None'
checkpoint_path = os.path.join(
log_dir, 'model.ckpt'
) # 'logdir-tacotron\\moon_2018-08-28_13-06-42\\model.ckpt'
#log(' [*] git recv-parse HEAD:\n%s' % get_git_revision_hash()) # hccho: 주석 처리
log('=' * 50)
#log(' [*] dit diff:\n%s' % get_git_diff())
log('=' * 50)
log(' [*] Checkpoint path: %s' % checkpoint_path)
log(' [*] Loading training data from: %s' % data_dirs)
log(' [*] Using model: %s' %
config.model_dir) # 'logdir-tacotron\\moon_2018-08-28_13-06-42'
log(hparams_debug_string())
# Set up DataFeeder:
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
# DataFeeder의 6개 placeholder: train_feeder.inputs, train_feeder.input_lengths, train_feeder.loss_coeff, train_feeder.mel_targets, train_feeder.linear_targets, train_feeder.speaker_id
train_feeder = DataFeederTacotron(coord,
data_dirs,
hparams,
config,
32,
data_type='train',
batch_size=config.batch_size)
test_feeder = DataFeederTacotron(coord,
data_dirs,
hparams,
config,
8,
data_type='test',
batch_size=config.num_test)
# Set up model:
is_randomly_initialized = config.initialize_path is None
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(hparams)
model.initialize(train_feeder.inputs,
train_feeder.input_lengths,
num_speakers,
train_feeder.speaker_id,
train_feeder.mel_targets,
train_feeder.linear_targets,
train_feeder.loss_coeff,
is_randomly_initialized=is_randomly_initialized)
model.add_loss()
model.add_optimizer(global_step)
train_stats = add_stats(model, scope_name='stats') # legacy
with tf.variable_scope('model', reuse=True) as scope:
test_model = create_model(hparams)
test_model.initialize(test_feeder.inputs,
test_feeder.input_lengths,
num_speakers,
test_feeder.speaker_id,
test_feeder.mel_targets,
test_feeder.linear_targets,
test_feeder.loss_coeff,
rnn_decoder_test_mode=True,
is_randomly_initialized=is_randomly_initialized)
test_model.add_loss()
test_stats = add_stats(test_model, model, scope_name='test')
test_stats = tf.summary.merge([test_stats, train_stats])
# Bookkeeping:
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=None, keep_checkpoint_every_n_hours=2)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
# Train!
#with tf.Session(config=sess_config) as sess:
with tf.Session() as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
if config.load_path:
# Restore from a checkpoint if the user requested it.
restore_path = get_most_recent_checkpoint(config.model_dir)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s at commit: %s' %
(restore_path, commit),
slack=True)
elif config.initialize_path:
restore_path = get_most_recent_checkpoint(
config.initialize_path)
saver.restore(sess, restore_path)
log('Initialized from checkpoint: %s at commit: %s' %
(restore_path, commit),
slack=True)
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
start_step = sess.run(global_step)
log('=' * 50)
log(' [*] Global step is reset to {}'.format(start_step))
log('=' * 50)
else:
log('Starting new training run at commit: %s' % commit,
slack=True)
start_step = sess.run(global_step)
train_feeder.start_in_session(sess, start_step)
test_feeder.start_in_session(sess, start_step)
while not coord.should_stop():
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss_without_coeff, model.optimize],
feed_dict=model.get_dummy_feed_dict())
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f]' % (
step, time_window.average, loss, loss_window.average)
log(message, slack=(step % config.checkpoint_interval == 0))
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.05f at step %d!' % (loss, step),
slack=True)
raise Exception('Loss Exploded')
if step % config.summary_interval == 0:
log('Writing summary at step: %d' % step)
feed_dict = {
**model.get_dummy_feed_dict(),
**test_model.get_dummy_feed_dict()
}
summary_writer.add_summary(
sess.run(test_stats, feed_dict=feed_dict), step)
if step % config.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' %
(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
if step % config.test_interval == 0:
log('Saving audio and alignment...')
num_test = config.num_test
fetches = [
model.inputs[:num_test],
model.linear_outputs[:num_test],
model.alignments[:num_test],
test_model.inputs[:num_test],
test_model.linear_outputs[:num_test],
test_model.alignments[:num_test],
]
feed_dict = {
**model.get_dummy_feed_dict(),
**test_model.get_dummy_feed_dict()
}
sequences, spectrograms, alignments, test_sequences, test_spectrograms, test_alignments = sess.run(
fetches, feed_dict=feed_dict)
#librosa는 ffmpeg가 있어야 한다.
save_and_plot(
sequences[:1], spectrograms[:1], alignments[:1],
log_dir, step, loss, "train"
) # spectrograms: (num_test,200,1025), alignments: (num_test,encoder_length,decoder_length)
save_and_plot(test_sequences, test_spectrograms,
test_alignments, log_dir, step, loss, "test")
except Exception as e:
log('Exiting due to exception: %s' % e, slack=True)
traceback.print_exc()
coord.request_stop(e)